What you'll learn
conceptually plan a coherent data model to plan and design enterprise-quality databases.
differentiate between UML and IE data models.
create databases with SQL and Microsoft Access.
Requirements
some knowledge of programming principles is strongly recommended.
Description
Truly effective database design depends on having a coherent data model to work from. This course will help you learn the theory and process of creating data models suitable for everything from small business to enterprise and data center environments. Michael Blaha will teach you how to plan and construct data models, as well as build upon those models through an actual database. You will start by learning about the data modeling development process, then jump into basic and advanced data modeling. From there, Michael will teach you how to create a UML data model, including finding classes, adding attributes, and simplifying the model. This video tutorial also covers how to translate a UML data model into an IE data model, model quality, the different kinds of data models, and database design. You will also learn how to create an SQL server database, an MS-Access database, and develop frameworks. Finally, Michael will teach you about data modeling patterns and database reverse engineering. Once you have completed this computer based training course, you will be fully capable of creating your own data models.
Overview
Section 1: Getting Started
Lecture 1 Important - Download These First - Working Files
Lecture 2 About The Course
Lecture 3 What Is A Database?
Lecture 4 What Is A Data Model?
Lecture 5 How To Access Your Working Files
Section 2: Data Model Development Process
Lecture 6 Data Model Inputs And Outputs
Lecture 7 Data Model Notations
Lecture 8 UML Versus IE - Conceptual, Logical And Physical
Section 3: Basic Data Modeling
Lecture 9 Class And Attribute
Lecture 10 Operation
Lecture 11 Domain
Lecture 12 Association
Lecture 13 IE Entity Type And Relationship Type
Lecture 14 Association Name
Lecture 15 Association End
Lecture 16 Multiplicity - UML
Lecture 17 Multiplicity - IE
Lecture 18 Generalization - UML
Lecture 19 Generalization - IE
Lecture 20 Abstract Versus Concrete Superclass
Lecture 21 Practical Tips
Lecture 22 Self Assessment Test
Section 4: Advanced Data Modeling
Lecture 23 Identity
Lecture 24 Derived Data
Lecture 25 Current Versus Historical Data
Lecture 26 Association Class
Lecture 27 Ordered Association
Lecture 28 Qualified Association - UML
Lecture 29 Qualified Association - IE
Lecture 30 Large Taxonomies
Lecture 31 Package
Lecture 32 Abridged UML Metamodel
Lecture 33 Abridged IE Metamodel
Lecture 34 Modeling Pitfalls
Lecture 35 Practical Tips
Lecture 36 Self Assessment Test
Section 5: Create A UML Data Model
Lecture 37 Problem Statement
Lecture 38 Finding Classes
Lecture 39 Finding Associations - Part 1
Lecture 40 Finding Associations - Part 2
Lecture 41 Finding Generalizations
Lecture 42 Iterating And Refining The Model - Part 1
Lecture 43 Iterating And Refining The Model - Part 2
Lecture 44 Adding Attributes
Lecture 45 Cleaning Up Layout
Lecture 46 Simplifying The Model
Lecture 47 Evolving A Model - Part 1
Lecture 48 Evolving A Model - Part 2
Lecture 49 Enterprise Architect Techniques - Part 1
Lecture 50 Enterprise Architect Techniques - Part 2
Lecture 51 Enterprise Architect Techniques - Part 3
Section 6: Translate A UML Data Model Into An IE Data Model
Lecture 52 Creating Subject Areas
Lecture 53 Creating Entity Types
Lecture 54 Creating Domains
Lecture 55 Adding Attributes - Part 1
Lecture 56 Adding Attributes - Part 2
Lecture 57 Creating Relationship Types - Part 1
Lecture 58 Creating Relationship Types - Part 2
Lecture 59 Creating Relationship Types - Part 3
Lecture 60 Subtyping
Lecture 61 Adding Alternate Keys
Lecture 62 Cleaning Up The Layout
Lecture 63 ERwin Techniques - Part 1
Lecture 64 ERwin Techniques - Part 2
Section 7: Model Quality
Lecture 65 Model Quality
Lecture 66 Normal Forms
Lecture 67 Constraints
Lecture 68 Hillard Graph Complexity
Lecture 69 Hoberman Data Model Scorecard
Section 8: Kinds Of Data Models
Lecture 70 Operational Data Models
Lecture 71 Enterprise Data Models
Lecture 72 Data Warehouses - Part 1
Lecture 73 Data Warehouses - Part 2
Lecture 74 Data Warehouses - Part 3
Lecture 75 Master Data Models
Section 9: Database Design
Lecture 76 Schema Adjustments
Lecture 77 Attribute Details - Part 1
Lecture 78 Attribute Details - Part 2
Lecture 79 Attribute Details - Part 3
Lecture 80 Primary And Alternate Keys
Lecture 81 Indexes
Lecture 82 Referential Integrity - Part 1
Lecture 83 Referential Integrity - Part 2
Lecture 84 Check Constraints - Part 1
Lecture 85 Check Constraints - Part 2
Lecture 86 Views
Lecture 87 Other Aspects Of Design
Lecture 88 Self Assessment Test
Section 10: Create A SQL Server Database
Lecture 89 Creating A New Database
Lecture 90 Executing Schema
Lecture 91 Inspecting Metadata
Lecture 92 Loading Sample Data
Lecture 93 Querying Sample Data
Section 11: Create An MS-Access Database
Lecture 94 Generating An ERwin Schema
Lecture 95 Creating Tables
Lecture 96 Creating Indexes
Lecture 97 Creating Constraints And Default Values
Lecture 98 Defining Foreign Keys
Lecture 99 Creating Views
Lecture 100 Loading Sample Data
Lecture 101 Querying Sample Data
Section 12: Software Engineering
Lecture 102 Development Frameworks
Lecture 103 Agile Data Modelling
Lecture 104 Documenting A Model - Part 1
Lecture 105 Documenting A Model - Part 2
Lecture 106 Presenting A Model
Section 13: Data Modeling Patterns
Lecture 107 Overview
Lecture 108 Tree - Hardcoded
Lecture 109 Tree - Simple
Lecture 110 Tree - Structured
Lecture 111 Tree - Overlapping
Lecture 112 Tree - Changing Over Time
Lecture 113 Tree - Degenerate Node and Edge
Section 14: Database Reverse Engineering
Lecture 114 Motives
Lecture 115 Comparison With Forward Engineering
Lecture 116 Outputs
Lecture 117 Inputs
Lecture 118 Process
Lecture 119 Principles
Lecture 120 Example - Part 1
Lecture 121 Example - Part 2
Section 15: Conclusion
Lecture 122 Wrap-Up
developers and IT professionals who want a thorough understanding of formal data concepts and models as they relate to database design.


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